Breakthrough in Autonomous Computing Ecosystem: Practical Path for Localization of High-Level Languages and Core Components

Core Challenges and Breakthrough Strategies in Local Computing Ecosystem Development

Developers in heterogeneous computing face two fundamental conflicts: balancing hardware performance optimization with development efficiency, and resolving ecosystem lock-in versus technical autonomy. Traditional GPU programming requires deep hardware architecture expertise - CUDA developers must manually manage thread blocks and shared memory, resulting in long development cycles and poor code portability.

The newly open-sourced heterogeneous computing component framework addresses these pain points through three innovations:

1. High-level language abstraction hiding hardware details
2. Instruction-level optimization for domestic chips
3. Three-layer architecture combining hardware abstraction with domain-specific language design

This approach draws inspiration from industry-proven patterns while implementing deep customization for domestic hardware characteristics. The LLVM-based intermediate representation optimization in the compilation toolchain enables precise adaptation to domestic chip instruction sets.

High-Level Language Compilation: Transitioning from CUDA to Domestic Platforms

TileLang Architecture Innovation

The framework’s core component, TileLang, features a three-layer architecture:

• Frontend Syntax Layer: Supports Python-style dynamic syntax with intuitive operator interfaces
• Intermediate Representation Layer: Builds tensor-based domain-specific IR with automatic parallelization
• Backend Code Generation: Custom optimizations for domestic chip instruction sets

This design eliminates the need for developers to manage register allocation or instruction scheduling. For matrix multiplication:

CUDA Implementation:

__global__ void matrixMul(float* C, float* A, float* B, int M, int N, int K) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    // Manual tiling implementation...
}

TileLang Implementation:

@tile_op
def matmul(A: Tensor[M,K], B: Tensor[K,N]) -> Tensor[M,N]:
    return tensor_contract(A, B, axes=[(-1,0)])

Performance Optimization Mechanisms

TileLang maintains hardware performance through three key technologies:

1. Auto-tuning Framework: Machine learning models predict optimal execution parameters
2. Instruction Fusion Engine: Combines fine-grained operations into single hardware instructions
3. Memory Hierarchy Optimization: Automates data movement between on-chip cache and global memory

Benchmark tests on training clusters show TileLang’s convolution operators achieving 92% of theoretical peak performance while improving development efficiency by 15% compared to manually optimized CUDA code.

Computing and Communication Components: Building High-Performance Operator Libraries

Computing Acceleration Libraries

The open-source framework provides five core libraries forming a complete operator matrix:

Library	Functionality	Key Optimizations
DeepGEMM	General matrix operations	Tiling computation + register reuse
TileKernels	Vector computation	Prefetch scheduling + loop unrolling
FlashMLA	Sparse attention	Compressed storage + parallel decoding

DeepGEMM’s dynamic tiling strategy adapts to varying matrix dimensions:

def optimized_gemm(A, B):
    M, K = A.shape
    K, N = B.shape
    block_size = determine_optimal_block(M,N,K)
    # Auto-generated tiling computation...

Distributed Communication Components

The DeepEP communication library achieves three breakthroughs for large-scale training:

1. Topology-aware Routing: Automatically detects rack-level network structure
2. Communication-Computation Overlap: Uses CUDA stream synchronization for zero-wait operations
3. Compressed Transmission Protocol: Reduces gradient data volume to 25% of original size

In 1,000-GPU cluster tests, DeepEP’s AllReduce operation latency decreased by 37% while effective bandwidth utilization reached 91%.

Ecosystem Migration Implementation Guide

Four-Step Migration Process

1. Syntax Conversion: Automatically rewrite CUDA kernels to TileLang
2. Operator Replacement: Substitute cuBLAS/cuDNN calls with corresponding libraries
3. Communication Reconstruction: Rebuild distributed training logic using DeepEP APIs
4. Performance Tuning: Optimize critical paths with auto-tuning framework

Migration Case Study

An AI lab migrating BERT training from NVIDIA to domestic platforms achieved:

• 70% reduction in code modifications
• 15% shorter end-to-end training time
• 40% lower hardware costs

Future Development Directions

The current open-source version provides foundational capabilities, with three priority areas for evolution:

1. Dynamic Graph Support: Enhance just-in-time compilation scenarios
2. Heterogeneous Scheduling: Enable CPU/NPU/DPU collaborative computing
3. Security Enhancements: Add hardware-level trusted execution environment support

This technical framework marks a new phase in domestic computing ecosystem development. By offering NVIDIA-equivalent development experiences, it enables smoother technology migration while laying groundwork for autonomous AI infrastructure. Enterprise users can now achieve supply chain security without compromising development efficiency or model performance, realizing both technical autonomy and business innovation objectives.